Ensure accurate representation, stronger discoverability, and greater influence across AI-driven search and decision journeys.
AI is transforming how consumers discover and evaluate brands. From AI-generated summaries to conversational search, buyer journeys are increasingly shaped without clicks.
This shift introduces a new challenge: brands no longer fully control how they are represented.
A hands-on executive strategy session by Microsoft and Visionet to identify high-value Agentic AI opportunities and define a responsible path toward enterprise-scale transformation.
In today’s rapidly evolving business landscape, organizations are under constant pressure to innovate faster, operate smarter, and deliver seamless digital experiences. Yet, legacy systems, fragmented data, and increasing complexity often stand in the way.
Visionet helps enterprises overcome these challenges by combining deep engineering expertise with AI-driven innovation, enabling them to transform, scale, and lead in a digital-first world.
Healthcare and life sciences organizations are under increasing pressure to improve outcomes, reduce administrative burden, and deliver seamless experiences across patients, providers, and partners. At the same time, AI investments are accelerating, but most remain stuck in pilots, disconnected from real operational impact.
The challenge isn’t a lack of ideas. It’s the absence of an operating model that can take AI from experimentation to enterprise-scale execution.
According to the ‘2025 McKinsey State of AI’ survey, 88% of organizations have an AI adoption strategy for at least one business function. However, few have successfully scaled those initiatives across the organization.
The report further reveals:
From recommendation engines to automated pricing dashboards, AI-powered tools have become common in retail. But agentic commerce is shifting the landscape again. AI agents are now doing tasks for shoppers and retailers, like helping them find products, making purchases, and handling post-purchase tasks.
Enterprises have invested heavily in modernizing contact centers. Cloud migrations, omnichannel platforms, bots, and analytics were meant to transform customer service.
Yet for many organizations, the results haven’t matched the investment.
Costs continue to rise. AI pilots stall. Customer experience remains inconsistent. Leadership teams find themselves reacting to problems instead of staying ahead of them.
A Microsoft & Visionet in-person executive workshop to identify high-impact AI agent use cases and move beyond experimentation.
Integrated Evidence Planning (IEP) has never lacked rigor. What it has lacked is coherence.
For years, IEP has been executed through structured templates, static plans, and cross-functional discussions focused on aligning studies, analyses, and publications to an asset milestone. The intent was sound. The execution was disciplined. Yet outcomes often fell short when evidence met real-world decision-making.
For years, Integrated Evidence Planning (IEP) has carried a paradox at its core. It is one of the most strategic levers in life sciences, yet the way it has been executed often feels operational, fragmented, and reactive. Teams build structured plans, populate spreadsheets, and align across functions with rigor and expertise. And yet, when a pivotal HTA decision challenges comparator choice, or when a payer questions endpoint relevance, the same thought resurfaces - we could have seen this coming.
